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Shuhei Kurita

23 papers hereh-index 7140 citations26 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author22
  • last author1

Across the 23 of 23 papers where every author was matched, so the position is known.

fields
  • cs.CV17
  • cs.RO3
  • cs.CL2
  • cs.AI1
same name
  • Shuhei Kurita — 11 papers, h 4
  • Shuhei Kurita — 7 papers, h 8
  • Shuhei Kurita — 3 papers, h 2
  • Shuhei Kurita — 2 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedCityNav: A Large-Scale Dataset for Real-World Aerial Navigation

1 citations · 3 across the 17 of their papers we have counts for

collaborators
Showing 2024 · cs.CVShow all

4 papers · 2 filters

cs.CV2024

Answerability Fields: Answerable Location Estimation via Diffusion Models

Daichi Azuma, Taiki Miyanishi, Shuhei Kurita +2

In an era characterized by advancements in artificial intelligence and robotics, enabling machines to interact with and understand their environment is a critical research endeavor…

cs.CV2024★ 1 cited

CityNav: A Large-Scale Dataset for Real-World Aerial Navigation

Jungdae Lee, Taiki Miyanishi, Shuhei Kurita +4

Vision-and-language navigation (VLN) aims to develop agents capable of navigating in realistic environments. While recent cross-modal training approaches have significantly improve…

cs.CV2024

Text-driven Affordance Learning from Egocentric Vision

Tomoya Yoshida, Shuhei Kurita, Taichi Nishimura +1

Visual affordance learning is a key component for robots to understand how to interact with objects. Conventional approaches in this field rely on pre-defined objects and actions,…

cs.CV2024

Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction

Koki Maeda, Shuhei Kurita, Taiki Miyanishi +1

Given the accelerating progress of vision and language modeling, accurate evaluation of machine-generated image captions remains critical. In order to evaluate captions more closel…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.